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Selective adversarial learning for mobile malware

- Khoda, Mahbub, Imam, Tasadduq, Kamruzzaman, Joarder, Gondal, Iqbal, Rahman, Ashfaqur

  • Authors: Khoda, Mahbub , Imam, Tasadduq , Kamruzzaman, Joarder , Gondal, Iqbal , Rahman, Ashfaqur
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 272-279
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  • Description: Machine learning models, including deep neural networks, have been shown to be vulnerable to adversarial attacks. Adversarial samples are crafted from legitimate inputs by carefully introducing small perturbation to the input so that the classifier is fooled. Adversarial retraining, which involves retraining the classifier using adversarial samples, has been shown to improve the robustness of the classifier against adversarial attacks. However, it has been also shown that retraining with too many samples can lead to performance degradation. Hence, a careful selection of the adversarial samples that are used to retrain the classifier is necessary, yet existing works select these samples in a randomized fashion. In our work, we propose two novel approaches for selecting adversarial samples: based on the distance from cluster center of malware and based on the probability derived from a kernel based learning (KBL). Our experiment results show that both of our selective mechanisms for adversarial retraining outperform the random selection technique and significantly improve the classifier performance against adversarial attacks. In particular, selection with KBL delivers above 6% improvement in detection accuracy compared to random selection. The method proposed here has greater impact in designing robust machine learning system for security applications. © 2019 IEEE.
  • Description: E1

Sliding window-based regularly frequent patterns mining over sensor data streams

- Rashid, Md Mamunur, Kamruzzaman, Joarder, Wasimi, Saleh

  • Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Wasimi, Saleh
  • Date: 2019
  • Type: Text , Conference paper
  • Relation: 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2019, Melbourne, 9 December 2019 through 11 December 2019
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  • Description: WSNs generate a large amount of data in the form of data stream; and mining these streams with well used support metric-based sensor association rule mechanism can result in extracting interesting knowledge. Support metric-based sensor association use occurrence frequency of pattern as criteria, but the occurrence frequency of a pattern may not be an appropriate criterion for finding significant patterns. However, temporal regularity in occurrence behavior can be considered as another important measure for assessing the importance of patterns in WSNs. A frequent pattern that occurs after regular intervals in WSNs called as regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely required in real-time applications, no such algorithm has been proposed yet. Therefore, in this paper we propose a novel tree structure, called regular frequent sensor pattern stream tree (RFSPS-tree) and a new technique, called regularly frequent sensor pattern mining of data stream (RFSPMS), using sliding window-based regularly frequent sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an RFSPS-tree, our RFSPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering regularly frequent sensor patterns over sensor data stream. © 2019 IEEE.

Survey of intrusion detection systems : techniques, datasets and challenges

- Khraisat, Ansam, Iqbal, Gondal, Vamplew, Peter, Kamruzzaman, Joarder

The co-evolution of cloud and IoT applications : recent and future trends

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2019
  • Type: Text , Book chapter
  • Relation: Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization 11 p. 213-234
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  • Description: ICT technologies have contributed to the advances in wireless systems, which provide seamless connectivity for worldwide communication. The growth of interconnected devices and the need to store, manage, and process the data from them has led to increased research on the intersection of the internet of things and cloud computing. The Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization is a pivotal reference source that provides the latest research findings and solutions for the design and augmentation of wireless systems and cloud computing. The content within this publication examines data mining, machine learning, and software engineering, and is designed for IT specialists, software engineers, researchers, academicians, industry professionals, and students.

Trusted autonomous vehicle : measuring trust using on-board unit data

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 787-792
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  • Description: Vehicular Ad-hoc Networks (VANETs) play an essential role in ensuring safe, reliable and faster transportation with the help of an Intelligent Transportation system. The trustworthiness of vehicles in VANETs is extremely important to ensure the authenticity of messages and traffic information transmitted in extremely dynamic topographical conditions where vehicles move at high speed. False or misleading information may cause substantial traffic congestions, road accidents and may even cost lives. Many approaches exist in literature to measure the trustworthiness of GPS data and messages of an Autonomous Vehicle (AV). To the best of our knowledge, they have not considered the trustworthiness of other On-Board Unit (OBU) components of an AV, along with GPS data and transmitted messages, though they have a substantial relevance in overall vehicle trust measurement. In this paper, we introduce a novel model to measure the overall trustworthiness of an AV considering four different OBU components additionally. The performance of the proposed method is evaluated with a traffic simulation model developed by Simulation of Urban Mobility (SUMO) using realistic traffic data and considering different levels of uncertainty. © 2019 IEEE.
  • Description: E1

An efficient data delivery mechanism for AUV-based Ad hoc UASNs

- Karmakar, Gour, Kamruzzaman, Joarder, Nowsheen, Nusrat

  • Authors: Karmakar, Gour , Kamruzzaman, Joarder , Nowsheen, Nusrat
  • Date: 2018
  • Type: Text , Journal article
  • Relation: Future Generation Computer Systems Vol. 86, no. (2018), p. 1193-1208
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  • Description: Existing 3D Underwater Acoustic Sensor Networks (UASNs) are either fixed having nodes anchored with the seabed or a combination of Autonomous Underwater Vehicles (AUVs) and a fixed UASN where AUVs are controlled to move along paths for data collection. Existing data delivery protocols for such AUV equipped networks are designed in a way where AUVs act as message ferries. These UASNs are deployed for a specific service such as asset (e.g., oil pipes, shipwreck) monitoring and event detection. For a coordinated data collection, to deploy a network for any service like information discovery in an ad hoc manner, it requires a 3D UASN consisting of only AUVs and the movement of an AUV needs to be controlled by another AUV through commands. To our knowledge, no such data delivery protocol required for a 3D UASN comprising only AUVs exists in the current literature that can efficiently handle data collection and delivery. To address this research gap, in this paper, an AUV-based technique for ad hoc underwater network, namely AUV-based Data Delivery Protocol (ADDP), is introduced which ensures data delivery within a given time-constraint by controlling node (i.e., AUV) movement at each hop through commands of a node. The performance of the proposed protocol has also been evaluated and compared with existing relevant protocols in terms of packet delivery ratio, routing overhead and energy consumption considering various network scenarios and sizes. Results exhibit outstanding performance improvement achieved by the proposed protocol for all metrics. © 2017 Elsevier B.V.

Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble

- Haque, Md Sarwar, Hassan, Md Rafiul, BinMakhashen, Galal, Owaidh, Abdullah, Kamruzzaman, Joarder

Detecting intrusion in the traffic signals of an intelligent traffic system

- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Saha, Tapash

  • Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Saha, Tapash
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 20th International Conference on Information and Communications Security, ICICS 2018; Lille, France; 29th-31st October 2018; published in Lecure Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11149 LNCS, p. 696-707
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  • Description: Traffic systems and signals are used to improve traffic flow, reduce congestion, increase travel time consistency and ensure safety of road users. Malicious interruption or manipulation of traffic signals may cause disastrous instants including huge delays, financial loss and loss of lives. Intrusion into traffic signals by hackers can create such interruption whose consequences will only increase with the introduction of driverless vehicles. Recently, many traffic signals across the world are reported to have intruded, highlighting the importance of accurate detection. To reduce the impact of an intrusion, in this paper, we introduce an intrusion detection technique using the flow rate and phase time of a traffic signal as evidential information to detect the presence of an intrusion. The information received from flow rate and phase time are fused with the Dempster Shaffer (DS) theory. Historical data are used to create the probability mass functions for both flow rate and phase time. We also developed a simulation model using a traffic simulator, namely SUMO for many types of real traffic situations including intrusion. The performance of the proposed Intrusion Detection System (IDS) is appraised with normal traffic condition and induced intrusions. Simulated results show our proposed system can successfully detect intruded traffic signals from normal signals with significantly high accuracy (above 91%).
  • Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Detecting splicing and copy-move attacks in color images

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur, Kahandawa, Gayan, Parvin, Nahida

  • Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur , Kahandawa, Gayan , Parvin, Nahida
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-7
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  • Description: Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.
  • Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018

Influence of clustering on the opinion formation dynamics in online social networks

- Das, Rajkumar, Kamruzzaman, Joarder, Karmakar, Gour

Mobile malware detection - An analysis of the impact of feature categories

- Khoda, Mahbub, Kamruzzaman, Joarder, Gondal, Iqbal, Imam, Tasadduq

Modelling majority and expert influences on opinion formation in online social networks

- Das, Rajkumar, Kamruzzaman, Joarder, Karmakar, Gour

  • Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
  • Date: 2018
  • Type: Text , Journal article
  • Relation: World Wide Web Vol. 21, no. 3 (2018), p. 663-685
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  • Description: Two most important social influences that shape the opinion formation process are: (i) the majority influence caused by the existence of a large group of people sharing similar opinions and (ii) the expert influence originated from the presence of experts in a social group. When these two effects contradict each other in real life, they may pull the public opinions towards their respective directions. Existing models on opinion formation utilised the idea of expertise levels in conjunction with the expressed opinions of the agents to encapsulate the expert effect. However, they have disregarded the explicit consideration of the majority effect, and thereby failed to capture the concurrent and combined impact of these two influences on opinion evolution. To represent the majority and expert impacts, we explicitly use the concept of opinion consistency and expertise level consistency respectively in an innovative way by capitalizing the notion of entropy in measuring the homogeneity of a group. Consequently, our model successfully captures the opinion dynamics under the concomitant influence of majority and expert. We validate the efficacy of our model in capturing opinion dynamics in a real world scenario using the opinion evolution traces collected from a widely used online social network (OSN) platform. Moreover, simulation results reveal the impact of the aforementioned effects, and confirm that our model can properly capture the consensus, polarization and fragmentation properties of public opinion. Our model is also compared with some recent models to evaluate its performance in both real world and simulated environments. © 2017, Springer Science+Business Media, LLC.

Passive detection of splicing and copy-move attacks in image forgery

- Islam, Mohammad, Kamruzzaman, Joarder, Karmakar, Gour, Murshed, Manzur, Kahandawa, Gayan

  • Authors: Islam, Mohammad , Kamruzzaman, Joarder , Karmakar, Gour , Murshed, Manzur , Kahandawa, Gayan
  • Date: 2018
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 555-567
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  • Description: Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.

Significance level of a big data query by exploiting business processes and strategies

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
  • Date: 2018
  • Type: Text , Conference paper
  • Relation: 13th Joint International Baltic Conference on Databases and Information Systems Forum and Doctoral Consortium, Baltic-DB and IS Forum-DC 2018 Vol. 2158, p. 63-73
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  • Description: Querying data is one of the most frequent activities in business organisations. The tasks involving queries for big data collection, extraction and analysis have never been easy, because to obtain the high quality responses, the expected outcome from these tasks need to be more accurate and highly relevant to a business organisation. The emergence of big data era has further complicated the task. The enormous volume of data from diverse sources and the variety of queries impose a big challenge on business organisations on how to extract deep insight from big data within acceptable time. Determining significance levels of queries based on their relevance to business organisations is able to deal with such challenge. To address this issue, up to our knowledge, there exists only one approach in the literature to calculate the significance level of a query. However, in this approach, only business processes are considered by manually selecting weights for core and non-core business processes. As the significance level of a query must express the importance of that query to a business organisation, it has to be calculated based on the consideration of business strategic direction, which requires the consideration of both business processes and strategies. This paper proposes an approach for the first time where the significance level of a query is determined by exploiting process contributions and strategy priorities. The results produced by our proposed approach using a business case study show the queries that are associated with more important business processes and higher priority strategies have higher significance levels. This vindicates the application of the significance level in a query to dynamically scale the semantic information use in capturing the appropriate level of deep insight and relevant information required for a business organisation. Copyright © 2018 for this paper by the papers' authors.

A rule based inference model to establish strategy-process relationship

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4544-4556
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  • Description: An effective relationship between business processes and their relevant strategies helps enterprises achieve their goals. As a business organisation changes quickly, business processes implement their relevant business operations for efficiency. It is important to know which business process achieves which business strategies dynamically. To the best of our knowledge, there exists a framework which aims to automatically determine the strategy-process relationship (Morrison et al. 2011). However, this framework can only work when the effect of the business process is known, but it is difficult to determine such effect accurately. Moreover, by optimising business processes to satisfy business strategies, higher efficiency may be achieved but there is a high chance of losing discriminative information. It therefore creates certain level of uncertainty in achieving accurate strategy-process relationship. To reduce this uncertainty and determine the relationship accurately between business processes and their relevant strategies as defined by business domain experts, in this paper, we introduce a rule-based inference model. This model not only helps business organisations realize which business processes need to be involved for the organisation to achieve their goals when strategies are made, but also reduces the possibility of losing important details from business process optimisation. We have developed a business case to validate our proposed model and the results show that our model can infer the relation accurately for each rule defined for the related business case.

Decentralized content sharing among tourists in visiting hotspots

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal

  • Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Journal of Network and Computer Applications Vol. 79, no. (2017), p. 25-40
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  • Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency.
  • Description: Content sharing with smart mobile devices using decentralized approach enables users to share contents without the use of any fixed infrastructure, and thereby offers a free-of-cost platform that does not add to Internet traffic which, in its current state, is approaching bottleneck in its capacity. Most of the existing decentralized approaches in the literature consider spatio-temporal regularity in human movement patterns and pre-existing social relationship for the sharing scheme to work. However, such predictable movement patterns and social relationship information are not available in places like tourist spots where people visit only for a short period of time and usually meet strangers. No works exist in literature that deals with content sharing in such environment. In this work, we propose a content sharing approach for such environments. The group formation mechanism is based on users' interest score and stay probability in the individual region of interest (ROI) as well as on the availability and delivery probabilities of contents in the group. The administrator of each group is selected by taking into account its probability of stay in the ROI, connectivity with other nodes, its trustworthiness and computing and energy resources to serve the group. We have also adopted an incentive mechanism as encouragement that awards nodes for sharing and forwarding contents. We have used network simulator NS3 to perform extensive simulation on a popular tourist spot in Australia which facilitates a number of activities. The proposed approach shows promising results in sharing contents among tourists, measured in terms of content hit, delivery success rate and latency. © 2016

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Information Sciences Vol. 379, no. (2017), p. 128-145
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  • Description: Wireless sensor networks (WSNs) will be an integral part of the future Internet of Things (loT) environment and generate large volumes of data. However, these data would only be of benefit if useful knowledge can be mined from them. A data mining framework for WSNs includes data extraction, storage and mining techniques, and must be efficient and dependable. In this paper, we propose a new type of behavioral pattern mining technique from sensor data called regularly frequent sensor patterns (RFSPs). RFSPs can identify a set of temporally correlated sensors which can reveal significant knowledge from the monitored data. A distributed data extraction model to prepare the data required for mining RFSPs is proposed, as the distributed scheme ensures higher availability through greater redundancy. The tree structure for RFSP is compact requires less memory and can be constructed using only a single scan through the dataset, and the mining technique is efficient with low runtime. Current mining techniques in the literature on sensor data employ a single memory-based sequential approach and hence are not efficient. Moreover, usage of the. MapReduce model for the distributed solution has not been explored extensively. Since MapReduce is becoming the de facto model for computation on large data, we also propose a parallel implementation of the RFSP mining algorithm, called RFSP on Hadoop (RFSP-H), which uses a MapReduce-based framework to gain further efficiency. Experiments conducted to evaluate the compactness and performance of the data extraction model, RFSP-tree and RFSP-H mining show improved results. (C) 2016 Elsevier Inc. All rights reserved.

Dynamic content distribution for decentralized sharing in tourist spots using demand and supply

- Kamruzzaman, Joarder, Karmakar, Gour, Gondal, Iqbal, Kaisar, Shahriar

  • Authors: Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal , Kaisar, Shahriar
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017; Valencia, Spain; 26th-30th June 2016 p. 2121-2126
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  • Description: Decentralized content sharing (DCS) is emerging as an important platform for sharing contents among smart mobile device users, where devices form an ad-hoc network and communicate opportunistically. Existing DCS approaches for tourist spot like scenarios achieve low delivery success rate and high latency as they do not focus on dynamic demand for contents which usually vary considerably with the number of visitors present or occurrence of some influencing events. The amount of available supply also changes because of the nodes leaving the area. Only way to improve content delivery service is to distribute the contents in strategic positions based on dynamic demand and supply. In this paper, we propose a dynamic content distribution (DCD) method considering dynamic demand and supply for contents in tourist spots. Simulation results validate the improvement of the proposed approach. © 2017 IEEE.
  • Description: 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017

Exclusive use spectrum access trading models in cognitive radio networks : A survey

- Hassan, Md Rakib, Karmakar, Gour, Kamruzzaman, Joarder, Srinivasan, Bala

  • Authors: Hassan, Md Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
  • Date: 2017
  • Type: Text , Journal article , Review
  • Relation: IEEE Communications Surveys and Tutorials Vol. 19, no. 4 (2017), p. 2192-2231
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  • Description: Spectrum frequency is a valuable resource for wireless communication but very limited in its availability. Due to the extensive use and ever increasing demand of spectrum bands by wireless devices and newer applications, unlicensed band is becoming congested, while licensed bands are found mostly underutilized. To solve this problem of spectrum scarcity, cognitive radio (CR) devices can share licensed bands opportunistically in several ways. We analyze the three main dynamic sharing models (commons, shared-use, and exclusive-use) proposed in literature with extensive analysis of the exclusive-use model, which is the most promising as it provides benefits to both licensed and unlicensed users. In this model, CR-enabled service providers, also known as secondary service providers, can buy or lease spectrum from licensed, known as primary service providers, for both short and long duration and gain exclusive rights to access the spectrum. In this survey paper, exclusive-use trading approaches, namely, game theory, market equilibrium, and classical, hybrid and other models are reviewed extensively and their characteristics and differences are highlighted and compared. We also propose possible future research directions on exclusive-use CR model. © 1998-2012 IEEE.

Exploiting evolving trust relationships in the modelling of opinion formation dynamics in online social networks

- Das, Rajkumar, Kamruzzaman, Joarder, Karmakar, Gour

  • Authors: Das, Rajkumar , Kamruzzaman, Joarder , Karmakar, Gour
  • Date: 2017
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017; Taipei, Taiwan; 27th-29th March 2017 p. 872-879
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  • Description: Mass participation of the members of a society in discussions to resolve issues related to a topic leads to forming public opinion. The timeline of the underlying dynamics goes through several distinguishable phases, and experiences transition from one to another. After initiated by concerned individuals, it draws active attention from almost everyone, and with time progression, people's participation starts declining as the issues are resolved or lost attraction. The existing works in the literature to capture the opinion formation process pay attention to model the dynamics in its active phase and thus ignore the other phases and the corresponding phase transitions. Trust relationships among the participants dynamically shape their interactions in different stages of the dynamics. Existing works fail to incorporate trust in defining the extent of influence one has on others, as they define the social relationships in the opinion space. To address this issue, we adopt simulated annealing to model the transitional behaviour of the dynamics, and then, amalgamate peoples relationships in the trust space with that in the opinion space to define the meta-heuristics of the algorithm for capturing the dynamical properties of the process. Finally, through simulation, we observe that our model is insightful in representing peoples' evolving behaviour in the different stages of opinion formation process, and consequently, can capture the various properties of the steady-state outcomes of the dynamics. © 2017 IEEE.
  • Description: Proceedings - International Conference on Advanced Information Networking and Applications, AINA